code: def get_coco(
      root,
      image_set,
      transforms,
      mode="instances",
      use_v2=False,
      with_masks=False,
  ):
      anno_file_template = "{}_{}2017.json"
      PATHS = {
          "train": (
              "train2017",
              os.path.join(
                  "annotations",
                  anno_file_template.format(mode, "train"),
              ),
          ),
          "val": (
              "val2017",
              os.path.join(
                  "annotations", anno_file_template.format(mode, "val")
              ),
          ),
          # "train": ("val2017", os.path.join("annotations", anno_file_template.format(mode, "val")))
      }
  
      img_folder, ann_file = PATHS[image_set]
      img_folder = os.path.join(root, img_folder)
      ann_file = os.path.join(root, ann_file)
  
      if use_v2:
          from torchvision.datasets import (
              wrap_dataset_for_transforms_v2,
          )
  
          dataset = torchvision.datasets.CocoDetection(
              img_folder, ann_file, transforms=transforms
          )
          target_keys = ["boxes", "labels", "image_id"]
          if with_masks:
              target_keys += ["masks"]
          dataset = wrap_dataset_for_transforms_v2(
              dataset, target_keys=target_keys
          )
      else:
          # TODO: handle with_masks for V1?
          t = [ConvertCocoPolysToMask()]
          if transforms is not None:
              t.append(transforms)
          transforms = T.Compose(t)
  
          dataset = CocoDetection(
              img_folder, ann_file, transforms=transforms
          )
  
      if image_set == "train":
          dataset = _coco_remove_images_without_annotations(dataset)
  
      # dataset = torch.utils.data.Subset(dataset, [i for i in range(500)])
  
      return dataset
  
file_path: vision/references/detection/coco_utils.py
imports: import os
  import torch
  import torch.utils.data
  import torchvision
  import transforms
  from pycocotools import mask
  from pycocotools.coco import COCO
label_desc: Use image node_id 5 for calling a global function with example usage: get_coco(root, image_set, transforms, mode, use_v2, with_masks) and returns: dataset